Legal claims defining the scope of protection. Each claim is shown in both the original legal language and a plain English translation.
2. The spoken dialogue system according to claim 1 , wherein a plurality of different success measures are generated.
A spoken dialogue system is designed to facilitate natural language interactions between users and automated systems, such as virtual assistants or customer service bots. A key challenge in such systems is accurately assessing the effectiveness of the dialogue to ensure the system meets user needs. This invention addresses this by generating multiple distinct success measures to evaluate different aspects of the dialogue's performance. These measures may include metrics like task completion rates, user satisfaction scores, dialogue efficiency, and accuracy of responses. By analyzing these diverse success measures, the system can identify strengths and weaknesses in its performance, allowing for continuous improvement. The system may also adapt its behavior based on these measures, such as adjusting response strategies or refining natural language processing models. This approach ensures that the dialogue system remains effective and user-friendly, enhancing overall interaction quality. The invention improves upon prior systems by providing a more comprehensive and nuanced evaluation framework, leading to better user experiences and more reliable automated interactions.
3. The spoken dialogue system according to claim 1 , wherein the feature vector further comprises features extracted from the system state updated based on the speech signal.
A spoken dialogue system processes user speech to generate responses, improving interaction quality by analyzing both the speech signal and the system's internal state. The system extracts features from the speech signal, such as acoustic and linguistic characteristics, to understand user input. Additionally, it updates its internal state based on the speech signal, reflecting changes in context, user intent, or dialogue history. These state updates are then used to generate a feature vector that combines speech-derived features with state-derived features. The combined feature vector enhances the system's ability to generate accurate and contextually appropriate responses. By incorporating system state updates, the system adapts dynamically to the conversation flow, improving natural language understanding and response generation. This approach ensures that responses are not only linguistically coherent but also contextually relevant, addressing challenges in maintaining coherent and engaging dialogues in real-world applications. The system's ability to integrate speech and state features allows for more sophisticated dialogue management, reducing errors and improving user satisfaction.
4. The spoken dialogue system according to claim 1 , wherein the output is an output configured to output a speech signal and wherein the information relating to the system action comprises: generating text specified by the system action; converting the text to speech and outputting the speech signal at the output.
A spoken dialogue system facilitates natural language interactions between users and automated systems. The system processes user inputs, determines appropriate system actions, and generates responses. A key challenge is ensuring that system responses are accurately conveyed to the user in a human-like manner. The system includes an output module configured to produce a speech signal. When the system determines an action to take, such as providing information or confirming a request, it generates text corresponding to that action. This text is then converted into speech using a text-to-speech (TTS) engine. The synthesized speech is output through the system's audio interface, allowing the user to hear the response. This approach ensures that system actions are communicated clearly and naturally, enhancing the user experience in dialogue-based applications. The system may also include additional components for input processing, action selection, and context management to support dynamic and context-aware interactions.
5. The spoken dialogue system according to claim 1 , wherein the acoustic features are one or more of an indication of energy of the speech signal, an indication of the pitch of the speech signal and spectral information for the speech signal.
A spoken dialogue system processes speech signals to facilitate natural language interactions. The system extracts acoustic features from the speech signal to analyze and interpret spoken input. These features include energy levels, pitch information, and spectral characteristics of the speech. Energy features indicate the amplitude or loudness of the speech signal, while pitch features represent the fundamental frequency, which helps distinguish between different speakers or emotional tones. Spectral information provides details about the frequency distribution of the speech signal, aiding in phoneme recognition and speech clarity assessment. By analyzing these acoustic features, the system improves speech recognition accuracy and enhances dialogue understanding, enabling more effective human-computer interactions. The system may also use these features to adapt responses based on speaker characteristics or environmental conditions, ensuring smoother and more contextually appropriate conversations. This approach enhances the robustness and naturalness of spoken dialogue systems in applications such as virtual assistants, customer service bots, and interactive voice response systems.
6. The spoken dialogue system according to claim 5 , wherein the spectral information comprises Mel-frequency cepstral coefficient values.
A spoken dialogue system processes audio input to facilitate natural language interactions. The system extracts spectral information from the audio signal to analyze and interpret spoken language. This spectral information includes Mel-frequency cepstral coefficient (MFCC) values, which represent the short-term power spectrum of the audio signal in a compact form. MFCCs are widely used in speech recognition because they effectively capture the characteristics of human speech, such as formants and vocal tract resonances. By using MFCC values, the system can accurately model and recognize speech patterns, improving the accuracy of speech recognition and dialogue processing. The system may also include components for noise reduction, feature extraction, and language modeling to enhance performance in various acoustic environments. The use of MFCCs ensures robust speech analysis, enabling the system to handle diverse accents, speech rates, and background noise conditions. This technology is applicable in virtual assistants, customer service automation, and other interactive voice applications.
8. The method according to claim 7 , wherein the performance indicator is generated using the success measure.
The invention relates to performance monitoring and evaluation in systems, particularly for assessing the effectiveness of processes or operations. The core problem addressed is the need for accurate and meaningful performance indicators that reflect real-world success metrics. Traditional performance indicators often fail to capture the true impact of operations, leading to misaligned incentives or ineffective improvements. The method involves generating a performance indicator based on a success measure, which quantifies the desired outcome of a process. This success measure is derived from operational data, such as system outputs, user feedback, or other relevant metrics. The performance indicator is then calculated using this success measure, ensuring that it directly reflects the actual success of the process rather than indirect or proxy metrics. This approach improves decision-making by providing a more accurate representation of performance, enabling better optimization and resource allocation. The method may also include steps such as collecting operational data, processing the data to extract relevant features, and applying statistical or machine learning techniques to derive the success measure. The performance indicator can be used for real-time monitoring, historical analysis, or predictive modeling to anticipate future performance trends. By grounding the performance indicator in a success measure, the method ensures that evaluations are aligned with actual outcomes, reducing the risk of misleading or biased assessments. This enhances the reliability and actionability of performance metrics in various applications, including industrial processes, business operations, and system management.
9. The method according to claim 8 , wherein the performance indicator is the reward value generated using the reward function, wherein the reward function is a function of the success measure.
The invention relates to a method for evaluating the performance of a system, particularly in reinforcement learning or optimization tasks. The method addresses the challenge of quantifying performance in dynamic environments where traditional metrics may not capture the true effectiveness of the system. The core of the invention involves using a reward function to generate a performance indicator, where the reward function is specifically designed to reflect a success measure relevant to the task. The success measure is a metric that directly assesses whether the system achieves its intended goals, such as task completion, accuracy, or efficiency. By deriving the performance indicator from this reward function, the method provides a more accurate and task-specific evaluation of the system's performance. This approach ensures that the performance indicator aligns closely with the actual objectives of the system, improving decision-making and optimization processes. The method can be applied in various domains, including robotics, autonomous systems, and machine learning, where performance evaluation is critical for iterative improvement and deployment. The use of a reward function tied to a success measure allows for adaptability and precision in performance assessment, addressing limitations of generic or static evaluation metrics.
10. The method according to claim 9 , wherein the reward function is also a function of one or more of the acoustic features.
This invention relates to reinforcement learning systems for audio processing, specifically improving the training of models using acoustic features. The problem addressed is the lack of effective integration of acoustic features into reward functions, which can lead to suboptimal model performance in tasks like speech recognition, music generation, or audio enhancement. The method involves training a reinforcement learning model where the reward function is dynamically adjusted based on one or more acoustic features extracted from audio signals. These features may include spectral characteristics, temporal patterns, or perceptual attributes like loudness or pitch. By incorporating these features into the reward function, the model learns to optimize its actions in a way that aligns with desired acoustic properties, improving accuracy and efficiency. The system first processes an input audio signal to extract relevant acoustic features. These features are then used to compute a reward value, which guides the model's learning process. The reward function is designed to be adaptive, meaning it can adjust its weighting of different acoustic features based on the specific requirements of the task. This ensures that the model prioritizes the most relevant features for optimal performance. The approach enhances traditional reinforcement learning by making the reward function context-aware, allowing the model to better handle variations in audio data and improve generalization across different acoustic environments. This method is particularly useful in applications where precise control over audio quality is critical.
11. The method of claim 7 , wherein a feature vector is generated using the acoustic features extracted from the speech signal, and inputted into a classifier, wherein the classifier is configured to output a success measure.
This invention relates to speech processing, specifically to evaluating the quality or success of a speech-related task using acoustic features and machine learning. The method extracts acoustic features from a speech signal, such as spectral, prosodic, or temporal characteristics, to generate a feature vector. This feature vector is then input into a trained classifier, which analyzes the features to produce a success measure. The success measure quantifies the performance or outcome of the speech task, such as speech recognition accuracy, speaker verification confidence, or speech synthesis naturalness. The classifier may be a neural network, support vector machine, or other machine learning model trained on labeled data to distinguish between successful and unsuccessful speech outcomes. The extracted acoustic features may include Mel-frequency cepstral coefficients (MFCCs), pitch contours, energy levels, or other speech-related metrics. The method enables automated assessment of speech processing systems, improving efficiency and reliability in applications like voice assistants, call centers, or speech synthesis. The classifier's output can be used to trigger corrective actions, adjust system parameters, or provide feedback for further training. This approach enhances the robustness and adaptability of speech processing technologies by leveraging machine learning to evaluate performance dynamically.
12. The method of claim 11 , wherein there is a plurality of classifiers, each generating a different success measure which are combined to form a single success measure which is included in the reward function.
This invention relates to machine learning systems that use multiple classifiers to evaluate performance and optimize decision-making. The problem addressed is the challenge of accurately assessing success in complex environments where a single metric may not capture all relevant factors. The solution involves using multiple classifiers, each generating a distinct success measure, which are then combined into a single success measure. This combined measure is incorporated into a reward function used to train or guide the system. The reward function helps optimize the system's behavior by reinforcing actions that lead to higher combined success measures. The use of multiple classifiers allows for a more nuanced and comprehensive evaluation of performance, improving decision-making in scenarios where different aspects of success need to be balanced. The invention is particularly useful in applications like autonomous systems, robotics, or any domain where performance evaluation is multi-faceted. By integrating diverse success measures, the system can adapt more effectively to varying conditions and objectives.
13. The method according to claim 11 , wherein the dialogue model comprises a policy model and a state tracker model, wherein the processor is further configured to: update the system state based on the input speech signal using the state tracker model, wherein the updated system state is the input to the policy model, wherein the updated system state comprises the success measure.
This invention relates to dialogue systems, specifically improving the handling of user interactions through a structured dialogue model. The system addresses the challenge of maintaining context and tracking user intent in conversational interfaces, ensuring accurate and coherent responses. The dialogue model includes a policy model and a state tracker model. The state tracker model processes an input speech signal to update the system's understanding of the current interaction state, including tracking user inputs and system responses. This updated state, which includes a success measure indicating the effectiveness of the interaction, is then fed into the policy model. The policy model uses this information to determine the next action or response in the dialogue, ensuring the system adapts dynamically to user needs. The success measure helps evaluate the dialogue's progress, allowing the system to adjust its strategy in real-time. This approach enhances the system's ability to handle complex conversations by maintaining a clear and evolving representation of the interaction state, improving user satisfaction and efficiency. The invention is particularly useful in applications like virtual assistants, customer service bots, and automated support systems where maintaining context and intent is critical.
14. A non-transitory carrier medium comprising computer readable code configured to cause a computer to perform the method of claim 7 .
A system and method for optimizing data processing in a distributed computing environment addresses inefficiencies in task scheduling and resource allocation. The invention focuses on improving computational performance by dynamically adjusting workload distribution across multiple processing nodes. The method involves analyzing task dependencies, predicting execution times, and redistributing tasks to balance the load and minimize idle time. The system monitors resource utilization in real-time, identifies bottlenecks, and reallocates tasks to underutilized nodes to enhance overall throughput. Additionally, the method includes a feedback mechanism that refines scheduling decisions based on historical performance data, ensuring continuous optimization. The non-transitory carrier medium, such as a storage device or memory, contains executable code that implements this method. The code is designed to be executed by a computer to perform the optimization process, including task analysis, dynamic redistribution, and performance monitoring. This approach reduces processing delays and improves efficiency in distributed computing environments by adaptively managing workloads and resources.
Unknown
November 10, 2020
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